Field of the Invention and Description of the Related Art
The present invention relates to an apparatus for recognizing various driving environments of a vehicle such as road conditions and traffic conditions.
Such an apparatus for recognizing the driving environments of a vehicle can be advantageously used to recognize various running conditions such as kinds of roads, e.g. freeways, expressways, roads in mountains and roads in cities, configurations of roads, e.g. zig-zag roads and straight roads, and traffic conditions, while operating conditions of drivers, e.g. operating speeds and characteristics of specific drivers and habits of a passenger or passengers are taken into account, and an automotive vehicle can be effectively controlled in accordance with the thus detected driving environments.
Heretofore, there have been proposed various apparatuses for recognizing driving environments of an automotive vehicle. For instance, in Japanese Patent Laid-open Publications Kokai Sho Nos. 59-200845 and 61-124759, there is disclosed an apparatus for recognizing the driving environments such as the inclination of roads and traffic conditions, in which characteristics of variations of physical amounts of vehicle parameters relating to the above mentioned driving environments are written in a program as a logic and the slope or inclination angle of roads and traffic conditions are detected by checking what a condition on the program is satisfied by the detected vehicle parameters.
In Japanese Patent Laid-Open Publication Kokai Hei No. 2-21058, there is further described another known apparatus for recognizing the driving environments of the automotive vehicle, in which the driving environments are recognized by utilizing a neural network having input layer, intermediate hidden layer and output layer. In this known apparatus, it is necessary to learn or write relations or combinations of various parameter input patterns and output patterns prior to the recognizing operation, but it is possible to recognize the driving environments by taking into account of non-specific matters such as habits of the drivers and passengers which could not be written by a usual program routine, so that its application will be extended in the feature.
In the known driving environments recognizing apparatuses, various driving conditions, e.g. various kinds of parameters of the vehicle running conditions are detected and the thus detected parameter values are applied to the input layer of the neural network as an input parameter pattern so as to excite or stimulate desired neurons on the input layer, and then the neural network operates to produce an output pattern on the output layer in accordance with the input parameter pattern by the associative operation. In this known apparatus, the recognition ratio or separation ratio can be made high. However, when a detected input parameter pattern is not identical with any of input parameter patterns which have been written in the neural network during the learning operation, although the detected input parameter pattern is similar to one of the learned input pattern of vehicle parameters, an output pattern which is not associated with said learned pattern of vehicle parameters might be derived. Therefore, when the driving environments are distinctly distinguished from each other such as the freeway running and the general way running, any problem occurs. However, when the driving environments are not clearly distinguished from each other, the recognition could not be performed correctly. For instance, the heavy traffic condition and light traffic condition have no definite boundary, the recognition result could not be obtained always correctly. In this manner, in the known driving environment recognizing apparatuses a versatility of a so-called self-organization is very low.